Methods and systems for anonymizing and providing access to transaction data

ABSTRACT

A computer-implemented method for providing presentable transaction data of a product to a user may include obtaining preliminary transaction data of one or more purchasers other than the user; generating itemized transaction data based on the preliminary transaction data; obtaining one or more translation codes from one or more transaction entities; generating standardized transaction data based on the itemized transaction data and the one or more translation codes; retrieving identification data from the standardized transaction data; generating anonymized transaction data based on the standardized transaction data by withholding the identification data; generating presentable transaction data based on the anonymized transaction data; and transmitting, to a device associated with the user, the presentable transaction data.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally toproviding access to anonymized item-level transaction data to thirdparties. More specifically, various embodiments of the presentdisclosure relate to anonymizing and analyzing item-level transactiondata, and to providing such anonymized transaction data to third parties(e.g., customers or other parties) for analysis and/or use in improvingshopping experiences.

BACKGROUND

When purchasing a product, a user (e.g., a consumer) may want to searchfor the lowest price. However, it may be difficult to find the cheapestprice online because not all pricing, discount, or coupon information isposted online in an aggregated manner. It may be even more difficult tofind or determine the lowest price during an in-store shoppingexperience. Additionally, even if a user finds a real-time low priceeither online or in store, it may be difficult for the user to knowwhether the real-time price could be lowered in the future.

Aspects of the present disclosure may overcome one or more of theseabove-referenced challenges. The background description provided hereinis for the purpose of generally presenting the context of thedisclosure. Unless otherwise indicated herein, the materials describedin this section are not prior art to the claims in this application andare not admitted to be prior art, or suggestions of the prior art, byinclusion in this section.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, methods and systems aredisclosed for providing presentable, anonymized transaction data of aproduct (i.e., “item-level” transaction data) to a user (e.g., acustomer or another third-party to the transaction). The methods andsystems may provide a mechanism that, among other things, allows usersto effectively search for a product with the most favorable price.

In an aspect, a computer-implemented method for providing presentabletransaction data of a product to a user may include obtaining, via oneor more processors, preliminary transaction data of one or morepurchasers other than the user, wherein the preliminary transaction dataof the one or more purchasers other than the user includes one or morereceipts associated with purchasing the product; generating, via the oneor more processors, itemized transaction data based on the preliminarytransaction data, wherein the itemized transaction data includes one ormore data categories associated with purchasing the product, wherein theone or more data categories include at least one of a location, a price,or a time associated with purchasing the product; obtaining, via the oneor more processors, one or more translation codes from one or moretransaction entities; generating, via the one or more processors,standardized transaction data based on the itemized transaction data andthe one or more translation codes; retrieving, via the one or moreprocessors, identification data from the standardized transaction data,wherein the identification data includes one or more of a name or anaccount number of a given purchaser of the one or more purchasers otherthan the user; generating, via the one or more processors, anonymizedtransaction data based on the standardized transaction data bywithholding the identification data; generating, via the one or moreprocessors, presentable transaction data based on the anonymizedtransaction data, wherein the presentable transaction data includes oneor more prices or one or more transaction locations associated withpurchasing the product; and transmitting, to a device associated withthe user, the presentable transaction data.

In another aspect, a computer-implemented method for providingpresentable transaction data of a product to a user may includeobtaining, via one or more processors, preliminary transaction data ofone or more purchasers other than the user, wherein the preliminarytransaction data of the one or more purchasers other than the userincludes one or more receipts associated with purchasing the product;generating, via the one or more processors, itemized transaction databased on the preliminary transaction data, wherein the itemizedtransaction data includes one or more data categories associated withpurchasing the product, wherein the one or more data categories includeat least one of a location, a price, or a time associated withpurchasing the product; obtaining, via the one or more processors, oneor more translation codes from one or more transaction entities;generating, via the one or more processors, standardized transactiondata based on the itemized transaction data and the one or moretranslation codes; retrieving, via the one or more processors,identification data from the standardized transaction data, wherein theidentification data includes one or more of a name or an account numberof a given purchaser of the one or more purchasers other than the user;generating, via the one or more processors, anonymized transaction databased on the standardized transaction data by withholding theidentification data; obtaining, via the one or more processors, one ormore selection criteria from the user, wherein the one or more selectioncriteria include at least one of a product name, a time range, or ageographic area associated with purchasing the product; generating, viathe one or more processors, presentable transaction data based on theanonymized transaction data and the one or more selection criteria,wherein the presentable transaction data includes one or more prices orone or more transaction locations associated with purchasing theproduct; generating, via the one or more processors, a purchasingrecommendation based on the anonymized transaction data via a trainedmachine learning algorithm, wherein the purchasing recommendationincludes at least one of a future time, a future price, and a futurelocation associated with purchasing the product; and transmitting, to adevice associated with the user, the presentable transaction data andthe purchasing recommendation.

In yet another aspect, a computer system for providing presentabletransaction data of a product to a user may include a memory storinginstructions; and one or more processors configured to execute theinstructions to perform operations. The operations may include obtainingpreliminary transaction data of one or more purchasers other than theuser, wherein the preliminary transaction data of the one or morepurchasers other than the user includes one or more receipts associatedwith purchasing the product; generating itemized transaction data basedon the preliminary transaction data, wherein the itemized transactiondata includes one or more data categories associated with purchasing theproduct, wherein the one or more data categories include at least one ofa location, a price, or a time associated with purchasing the product;obtaining one or more translation codes from one or more transactionentities; generating standardized transaction data based on the itemizedtransaction data and the one or more translation codes; retrievingidentification data from the standardized transaction data, wherein theidentification data includes one or more of a name or an account numberof a given purchaser of the one or more purchasers other than the user;generating anonymized transaction data based on the standardizedtransaction data by withholding the identification data; generatingpresentable transaction data based on the anonymized transaction data,wherein the presentable transaction data includes one or more prices orone or more transaction locations associated with purchasing theproduct; and transmitting, to a device associated with the user, thepresentable transaction data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodimentsand, together with the description, serve to explain the principles ofthe disclosed embodiments.

FIG. 1 depicts an exemplary system infrastructure, according to one ormore embodiments.

FIG. 2 depicts a flowchart of an exemplary method of providingtransaction data of a product to a user, according to one or moreembodiments.

FIG. 3 depicts a flowchart of another exemplary method of providingtransaction data of a product to a user, according to one or moreembodiments.

FIG. 4 shows graphical representations of an exemplary user interfaceprovided on a user/purchaser device.

FIG. 5 depicts an example of a computing device, according to one ormore embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection. Both the foregoing general description and the followingdetailed description are exemplary and explanatory only and are notrestrictive of the features, as claimed.

In this disclosure, the term “based on” means “based at least in parton.” The singular forms “a,” “an,” and “the” include plural referentsunless the context dictates otherwise. The term “exemplary” is used inthe sense of “example” rather than “ideal.” The terms “comprises,”“comprising,” “includes,” “including,” or other variations thereof, areintended to cover a non-exclusive inclusion such that a process, method,or product that comprises a list of elements does not necessarilyinclude only those elements, but may include other elements notexpressly listed or inherent to such a process, method, article, orapparatus. Relative terms, such as, “substantially” and “generally,” areused to indicate a possible variation of ±10% of a stated or understoodvalue.

In the following description, embodiments will be described withreference to the accompanying drawings. As will be discussed in moredetail below, in various embodiments, data such as preliminarytransaction data of one or more purchasers other than the user, itemizedtransaction data, one or more translation codes, standardizedtransaction data, identification data, and/or anonymized transactiondata may be used to determine presentable transaction data of a product.

As discussed herein, preliminary transaction data may include anyoriginal or raw transaction data that are obtained during an action oftransaction (e.g., the language on a receipt during purchasing aproduct). Itemized transaction data may include any transaction datagenerated based on the preliminary transaction data. For example, theitemized transaction data may include any transaction details includinga quantity purchased, a brand name, a shorthand version of the productdescription, discounts applied, and/or an inventory number. Standardizedtransaction data may include transaction data obtained from preliminarytransaction data that can be understood by a user (e.g., any readablelanguage associated with a transaction) and/or used by, e.g., a computersystem, to combine, cross-reference, or index transaction information ortransaction data stored in one or more databases. Standardizedtransaction data may include, e.g., translations of preliminary and/oritemized transaction data into standardized codes, lists, names, etc. tobetter support combining, indexing and/or cross-referencing multipletransactions from different sources. The standardized transaction datamay include private standardized transaction data used to accesstransaction information stored in one or more databases (e.g., databasekeys), or may include public standardized transaction data, such asInternational Standard Book Numbers (ISBNs) corresponding to items inthe transaction. Presentable transaction data may include anytransaction data that can be presented to (e.g., via a user device),and/or understood by a user. In some embodiments, presentabletransaction data may include any transaction data that is intended toserve a purpose to a user, and/or that can be presented to a userwithout breaching the privacy and/or confidentiality of another user(e.g., a user who participated in a transaction). In some embodiments,presentable transaction data may also be used (e.g., by a user or by acomputer system) to determine a purchasing recommendation for a user.

FIG. 1 is a diagram depicting an example of a system environment 100according to one or more embodiments of the present disclosure. Thesystem environment 100 may include a computer system 110, a network 130,one or more data resources 140 (e.g., preliminary transaction data), anda user/purchaser device (or a device associated with a user) 150. Theone or more data resources 140 may include transaction entities such asthe financial services providers 141 (including, e.g., enhanced merchantservice agencies), the online resources 142, and the merchants 143.These components may be connected to one another via the network 130.

The computer system 110 may have one or more processors configured toperform all or part(s) of methods described in this disclosure. Thecomputer system 110 may include one or more modules, models, or engines.The one or more modules, models, or engines may include an algorithmmodel 112, a notification engine 114, a data processing module 116, atransaction tracker module 118, an identification module 120, and/or aninterface/API module 122, which may each include hardware and/orsoftware components stored in the computer system 110. The computersystem 110 may be configured to utilize the one or more modules, models,or engines when performing various methods described in this disclosure.In some examples, the computer system 110 may include a cloud computingplatform with scalable resources for computation and/or data storage,and may run one or more applications on the cloud computing platform toperform various computer-implemented methods described in thisdisclosure. In some embodiments, some of the one or more modules,models, or engines may be combined to form fewer modules, models, orengines. In some embodiments, one or more modules, models, or enginesmay be separated into separate, more numerous modules, models, orengines. In some embodiments, some of the one or more modules, models,or engines may be removed while others are added.

The algorithm model 112 may, e.g., include one algorithm model, or mayinclude a plurality of algorithm models. The algorithm model 112 mayinclude, e.g., a trained machine learning model. Details of thealgorithm model 112 are described elsewhere herein. The notificationengine 114 may be configured, e.g., to generate and communicate (e.g.,transmit) one or more notifications (e.g., presentable transaction data)to a user/purchaser device 150 through the network 130. The dataprocessing module 116 may be configured, e.g., to monitor, track, clean,process, itemize, anonymize, or standardize data (e.g., preliminarytransaction data) received in the computer system 110. One or morealgorithms run by the data processing module 116 may be used to clean,process, itemize, anonymize, or standardize the data. The transactiontracker module 118 may be configured to, e.g., monitor or tracktransaction information (e.g., preliminary transaction data). Forexample, the transaction tracker module 118 may retrieve, store, andotherwise aggregate or manage current or historical transaction data orinformation from the financial services providers 141, the onlineresources 142, and the merchants 143. The identification module 124 may,e.g., manage identification information for each user or purchaseraccessing the computer system 110, possibly including, but not limitedto, actual names, usernames, passwords, contact information, andadditional information pertaining to the user or the purchaser. Theidentification information may further or alternatively includepreference information, demographic information, previous purchaseinformation, and/or other data related to the particular user orpurchaser. In one implementation, the identification informationassociated with each user or purchaser may be stored in, and/orretrieved from, one or more components of the data storage associatedwith the computer system 110. The interface/API module 122 may, e.g.,allow the user or purchaser to interact with one or more modules,models, or engines of the computer system 110. For example, theinterface/API module 122 may include a search engine that a user caninteract with to search information of a product.

The computer system 110 may be configured to receive data from varioussources (e.g., the financial services providers 141, the onlineresources 142, the merchants 143, and/or a user/purchaser device 150) inthe system environment 100 through the network 130. The computer system110 may further be configured to utilize the received data by inputtingthe received data into the algorithm model 112 to produce a result.Information indicating the result may, e.g., be transmitted to auser/purchaser device 150 over the network 130, and/or may be stored on,e.g., the computer system 110. In some examples, the computer system 110may be referred to as a server system that provides a service includingproviding the information indicating the result to a user/purchaserdevice 150.

The network 130 may be any suitable network or combination of networksand may support any appropriate protocol suitable for communication ofdata to and from the computer system 110 and/or the other components ofthe system environment 100. The network 130 may include a public network(e.g., the internet), a private network (e.g., a network within anorganization), or a combination of public and/or private networks. Thenetwork 130 may comprise one or more networks that connect devicesand/or components in the network layout to allow communication betweenthe devices and/or components. For example, the network 130 may beimplemented as the Internet, a wireless network, a wired network (e.g.,Ethernet), a local area network (LAN), a Wide Area Network (WANs),Bluetooth, Near Field Communication (NFC), or any other type of networkthat provides communications between one or more components of thenetwork layout. In some embodiments, the network 130 may be implementedusing cell and/or pager networks, satellite, licensed radio, or acombination of licensed and unlicensed radio.

The financial services providers 141 may include one or more entitiessuch as a bank, credit card issuer, merchant services provider, or othertype of financial service entity. In some examples, the financialservices providers 141 may include one or more merchant servicesproviders that provide the merchants 143 with the ability to acceptelectronic payments, such as payments using credit cards and debitcards. In some embodiments, a financial services provider 141 maycollect and store transaction information (e.g., preliminary transactiondata), one or more translation codes, and/or identification data, andtransmit presentable transaction data to the user.

The online resources 142 may include any resources available on or viathe Internet, and/or resources that exchange information over theinternet, such as a webpage or website, email, apps, or social networksites. The online resources 142 may include electronic transaction data(e.g., electronic receipts) held by a user, a purchaser, or otherparties. The online resources 142 may be provided by manufacturers,retailers, consumer promotion agencies, and other entities. The onlineresources 142 may include other computer systems, such as web servers,that are accessible by the computer system 110. The online resources 142may be configured to provide any information regarding a user, apurchaser, and/or a transaction, including, but not limited to, a user'sor purchaser's profile (e.g., gender, age, social status, list offriends, contacts, calendar, etc.), user's or purchaser's preferences(e.g., hobbies, aspirations, etc.), a time stamp, a geographic location,a transaction amount, a product of the transaction, a discount of thetransaction, or any historical or current transaction informationregarding a transaction.

The merchants 143 may each be an entity that provides products. In thisdisclosure, the term “product,” in the context of products offered by amerchant, encompasses both goods and services, as well as products thatare a combination of goods and services. A merchant may be, for example,a retailer, a vehicle dealer, a grocery store, an entertainment venue, aservice provider, a restaurant, a bar, a non-profit organization, acharitable organization, or other type of entity that provides productsthat a consumer or a user may consume. A merchant 143 may have one ormore venues that a consumer or a user physically visits in order toobtain the products (goods or services) offered by the merchant.

The merchants 143 and/or the financial services providers 141 may eachinclude one or more computer systems configured to gather, process,transmit, and/or receive data. In general, whenever any of the merchants143 and the financial services providers 141 is described as performingan operation of gathering, processing, transmitting, or receiving data,it is understood that such operation may be performed by a computersystem thereof. In general, a computer system may include one or morecomputing devices, as described in FIG. 5 below.

The user/purchaser device 150 (e.g., a device associated with a user)may operate a client program, also referred to as a user application,used to communicate with the computer system 110. This user applicationmay be used to provide information to the computer system 110 and toreceive information from the computer system 110. In some examples, theuser application may be a mobile application that is run on theuser/purchaser device 150. In some embodiments, the user application maybe provided by and/or associated with one or more data resources 140. Insome examples, the user/purchaser device 150 may be an electronic mobiledevice (e.g., smartphone, tablet, pager, personal digital assistant(PDA)), a computer (e.g., laptop computer, desktop computer, server), ora wearable device (e.g., smartwatches). In further examples, theuser/purchaser device 150 may include any other device capable ofproviding or receiving data. The user/purchaser device 150 mayoptionally be portable. The user/purchaser device 150 may optionally behandheld. The user/purchaser device 150 may be a device capable ofconnecting to the network 130, or any other network such as a local areanetwork (LAN), wide area network (WAN) such as the Internet, atelecommunications network, a data network, or any other type ofnetwork. The user/purchaser device 150 may be utilized to obtainidentification of the user or the purchaser and/or authenticate the useror the purchaser.

The computer system 110 may be owned, operated, and/or part of an entity105, which may be any type of company, organization, or institution. Insome examples, the entity 105 may be a financial services provider. Insuch examples, the computer system 110 may have access to datapertaining to consumer transactions through a private network within theentity 105, or otherwise related to the entity 105. For example if theentity 105 is a card issuer, the entity 105 may collect and storetransactions involving a credit card or debit card issued by the entity105. In such examples, the computer system 110 may still receivetransaction information from other financial services providers 141.

FIG. 2 is a flowchart illustrating a method for providing presentabletransaction data of a product to a user, according to one or moreembodiments of the present disclosure. The method may be performed by,e.g., part or all of the computer system 110 (e.g., the algorithm model112), the network 130, the one or more data resources 140, and/or theuser/purchaser device 150.

Step 201 may include obtaining, via one or more processors, preliminarytransaction data of one or more purchasers other than the user. Thepreliminary transaction data of the one or more purchasers other thanthe user may include one or more purchasing records, such as partial orfull receipts, statements, or other records associated with purchasingthe product. The preliminary transaction data may include transactioninformation associated with purchasing the product, including, but notlimited to, a transaction location, a transaction time (e.g., a date,time of day, season, time of year, etc.), a product identifier (e.g., aserial number, brand name, or other name to identify the product), atransaction amount, characteristics of the product (e.g., a description,weight, or size of the product), a merchant identification (e.g., acode, name, or other description to identify the merchant), and/ortransaction vehicle information associated with purchasing the product(e.g., a credit card number, account, card type, account type, or othertransaction vehicle information associated with purchasing the product).

The preliminary transaction data may further include any informationregarding the one or more purchasers other than the user, including, butnot limited to, a purchaser name and/or identifier, contact information(e.g., address, phone numbers, e-mail addresses, etc.), demographicinformation (e.g., age, gender, marital status, income level,educational background, number of children in household, etc.),transaction preferences (preferences or reviews regarding favoriteproducts and/or services, favorite department stores, etc.), andprevious transaction information. The previous transaction informationregarding the one or more purchasers other than the user may include aprior transaction time, a prior transaction location, spending profileof the one or more purchasers other than the user, past spending levelson goods/services sold by various manufacturers or merchants, afrequency of shopping by the one or more purchasers other than the userat one or more retail outlets, store loyalty exhibited by the one ormore purchasers other than the user, how much the one or more purchasersother than the user spend in an average transaction, how much the one ormore purchasers other than the user have spent on a particularcollection/category, how often the one or more purchasers other than theuser shop in a particular store or kind of store, an estimated profitmargin on goods previously purchased, and/or online or offline stores atwhich the one or more purchasers other than the user have purchaseditems.

The preliminary transaction data may further include reward dataassociated with purchasing the product. The reward data associated withpurchasing the product may include, but is not limited to a cash backamount or agreement, a discounted price or discount percentage, acustomer loyalty reward, an incentive to purchase the product again, anincentive to buy another product similar to the purchased product, anincentive to promote the product, a new customer incentive, a reward toswitch away from another retailer or manufacturer, an incentiveassociated with a particular level or degree of engagement between theuser/purchaser and the product or brand, or a reward for a customer thathas proven to be more lucrative than others. The reward may bepresented, e.g., on a receipt or in a record associated with a productpurchase. In this situation, the reward may be recognized and/orobtained as the preliminary transaction data via a natural languageprocessing algorithm.

The step of obtaining the preliminary transaction data of one or morepurchasers other than the user may include obtaining the preliminarytransaction data of one or more purchasers other than the user from atransactional entity over a network (e.g., the network 130). Thetransactional entity may include, e.g., one or more merchants 143,financial services providers 141, or online resources 142. For instance,a purchaser other than the user may upload one or more receiptsassociated with purchasing a product to a server or database associatedwith a financial service provider via a user application presented on adevice associated with the purchaser (e.g., the purchaser uses thedevice to take one or more images of the receipt). In some embodiments,if the entity 105 operating the computer system 110 is a card issuer orother financial services provider that is involved in processing paymenttransactions, the computer system 110 may have access to suchtransactional data associated with one or more purchasers other than theuser directly or through a private network within entity 105, and mayutilize such information in addition to or alternatively to informationfrom other financial services providers 141. In some embodiments, if theentity 105 operating the computer system 110 is a card issuer or otherfinancial services provider that is involved in processing paymenttransactions, the entity may include one or more databases to store suchpreliminarily transaction data (e.g. including purchase location,timing, purchaser identity). In this situation, the step of obtainingthe preliminary transaction data of one or more purchasers other thanthe user may include obtaining the preliminary transaction data from theone or more databases. In some embodiments, during the process ofobtaining the preliminary transaction data, a user/purchaser mayauthorize the transactional entity to upload or share the preliminarilytransaction data with other entities (e.g., financial servicesproviders) based on incentives (e.g., a discount or coupon associatedwith purchasing a product) provided by the transactional entity. Forinstance, a user/purchaser may be incentivized to authorize a merchantto share the preliminary transaction data associated with purchasing aproduct with a financial services provider.

The step of obtaining the preliminary transaction data may includeproviding one or more incentives to the one or more purchasers otherthan the user. The one or more incentives may include, but are notlimited to, a cash back or a coupon associated with purchasing theproduct if the one or more purchaser other than the user provides thepreliminary transaction data. For instance, in order to incentivize theone or more purchasers other than the user to provide the preliminarytransaction data (e.g., upload a receipt), the one or more purchasersother than the user may be provided with a coupon code. In someembodiments, the one or more incentives may include an option to obtainpresentable transaction data or a purchasing recommendation in thefuture. For instance, in order for a purchaser or a user to obtainaccess to presentable transaction data or a purchasing recommendation ona display of a user/purchaser device 150, the purchaser or the user mayneed to provide preliminary transaction data.

Step 202 may include generating, via the one or more processors,itemized transaction data based on the preliminary transaction data. Thestep of generating the itemized transaction data may include dividing orotherwise converting preliminary transaction data into one or more itemcategories (e.g., transaction data pertaining to individual items). Agiven item category of the one or more item categories may pertain to anindividual item that is purchased (in a quantity of one or more) duringthe transaction. According to the present disclosure, an “item” may beany object or service (e.g., an item of clothing, a luxury item, anovernight hotel stay, an airline ticket, a cleaning service, etc.). Agiven item category of the one or more item categories may also includeone or more data categories, each of which may include informationassociated with the individual item (e.g., identification of thepurchaser and/or seller of the item, transaction vehicle information,the price of the individual item, a quantity of the item purchased, anydiscounts or coupons applied with respect to the item, etc.). Forinstance, the preliminary transaction data may include a receipt, andthe receipt may include a listing of three purchased video games and onepurchased console. In this situation, the itemized transaction data mayinclude four item categories (pertaining to each of the three purchasedvideo games and the one purchased console), where each item category mayinclude one or more data categories associated with the item to which itpertains. The one or more data categories may include, e.g., a location,a price, a time, a product identifier (e.g., a name of the product, or acode associated with the product), a merchant identification (e.g., aname of the merchant), a buyer identification (e.g., buyer's name,contact information or demographic information), a reward (e.g., adiscount, coupon, cash back, or incentive associated with the product),transaction vehicle information (e.g., a credit card or account number)associated with purchasing the product, and/or any other data relevantto a purchased item. In some embodiments, instead of or in addition tobeing divided or otherwise converted into one or more item categories,the preliminary transaction data may be divided or otherwise convertedbased on one or more data categories. One or more algorithms may be usedto generate the itemized transaction data based on the preliminarytransaction data. For instance, the one or more algorithms may classifythe preliminary transaction data into one or more item categories, whereeach item category may be associated with a product and include one ormore data categories. In an example, if the preliminary transaction dataincludes a receipt indicating that customer A buys a dress for $50, ashirt for $20, and a hat for $10, then the itemized transaction data maybe classified into multiple item categories—one corresponding to thedress, one corresponding to the shirt, and one corresponding to the hat.Optionally, each item category may include additional data, such as abuyer identification (e.g., customer A), a product identifier (e.g., thename, brand, code, and/or description of the item of clothing), and aprice (e.g., $50, $20, or $10).

In some embodiments, the one or more algorithms may be used to add moredata to the preliminary transaction data or itemized transaction data.For instance, the preliminary transaction data may not include anaddress associated with purchasing the product, but may include a nameof a merchant. In this situation, the one or more algorithms may provideand add the address to the preliminary transaction data or the itemizedtransaction data associated with purchasing the product based oninformation regarding the purchaser (e.g., a residence of the purchaseror a favorite department store of the purchaser) and the name of themerchant. In another example, a purchaser may purchase a product via acredit card issued by a financial services provider, and the preliminarytransaction data may not include a time associated with purchasing theproduct, but may include information identifying the product. In thissituation, the one or more algorithms may access one or more databasesassociated with the financial services provider to obtain additionaldata (e.g., a time) associated with purchasing the product and add suchadditional data to the preliminary transaction data or the itemizedtransaction data.

Step 203 may include obtaining, via the one or more processors, one ormore translation codes from one or more transaction entities. In someembodiments, the one or more translation codes can be generated orprovided internally by the computer system 110, one or more sources 140,and/or by an entity 105 issuing a credit card involved in thetransaction of purchasing a product. The one or more translation codesmay be used to translate the itemized transaction data into standardizedtransaction data. The one or more transaction entities may include oneor more enhanced merchant service agencies. Enhanced merchant serviceagencies may include any financial services providers or transactionentities that can process the itemized transaction data or preliminarytransaction data, and/or provide standardized transaction data. In someembodiments, any enhanced merchant service agency may be a transactionentity or a financial services provider. The enhanced merchant serviceagencies may include, e.g., commercial banks (e.g., Capital One® Bank)or financial transaction platforms (e.g., PayPal®). The enhancedmerchant service agencies may include the financial services providersthat provide a user or a purchaser with a transaction vehicle (e.g., acredit card or a credit account) used to buy the product. In thissituation, the enhanced merchant service agencies may be able to gathermore information regarding the product or the transaction of purchasingthe product than the preliminary transaction data (e.g., a receipt)provided by the purchaser. The one or more translation codes may beassociated with one or more transaction categories, including, but notlimited to, a payment category (e.g., translation codes for all paymentactivities that relate to transfer of funds between parties), a foreignexchange category (e.g., translation codes related to a foreign exchangerate), or an account management category (e.g., translation codestransmitting funds from one financial services provider to anotherfinancial services provider). Additionally or alternatively, the one ormore translation codes may be associated with translating data in anitem category or a data category into a standardized format. Forexample, the one or more translation codes may be used to translate astockkeeping unit (SKU), International Standard Book Number (ISBN),abbreviated term, or other type of item identifier into a fuller listingof an item appearing in transaction data. For instance, a translationcode may indicate that a merchant-specific code for an item (e.g., astore code “xb578”) may be translated into a more generally recognizablename (e.g., “Fun Racing Game—Console One.”) As another example, atranslation code may indicate that an ISBN “9780747532743” may betranslated to “Awesome Wizard Book, Volume1.” Alternatively, atranslation code may indicate that a merchant-specific listing of“Awesome Wizard Book, Volume 1” may be translated to a correspondingISBN, “9780747532743.” The one or more translation codes may thus beused to standardize an abbreviated or situation-specific term or othertypes of item identifier into a different format for use in standardizedtransaction data, anonymized transaction data, and/or presentabletransaction data. Translation codes may likewise be used to standardizeinformation stored in data categories, such as merchant names,locations, addresses, and the like.

Step 204 may include generating, via the one or more processors,standardized transaction data based on the itemized transaction data andthe one or more translation codes. One or more algorithms may be used togenerate the standardized transaction data based on the itemizedtransaction data and the one or more translation codes. The one or morealgorithms may translate the itemized transaction data into standardizedtransaction data based on the one or more translation codes. Forinstance, the itemized transaction data may include a specific merchantshown as “StoreName XYZ,” and the one or more algorithms may translate“StoreName XYZ” into standardized transaction data, which may read“StoreName at 99 H St NW, Washington, D.C.” via one or more translationcodes. In another example, the itemized transaction data provided bypurchaser A may include a specific product shown as “Product135A,” andthe itemized transaction data provided by purchaser B may include aspecific product shown as “Product135B.” After translation of“Product135A” using an applicable translation code, and “Product135B”using either the same or a different applicable translation code, viathe one or more algorithms, the standardized transaction data may showthat “Product135A” and “Product135B” are both references to an itemknown as “Product A,” and are therefore the same item.

Step 205 may include retrieving, via the one or more processors,identification data from the standardized transaction data. Theidentification data may include, for example, one or more of a name oran account number of a given purchaser of the one or more purchasersother than the user. The identification data may further include, forexample, a time associated with purchasing the product, an address, anincome range, a medical history, a criminal background, or a socialsecurity number of the given purchaser of the one or more purchasersother than the user. The identification data also or alternatively mayinclude biometric data, such as a fingerprint, palm veins, facerecognition, DNA, palm print, hand geometry, iris recognition, retinapattern, odor/scent, and/or behavioral characteristics, such as typingrhythm, gait, and/or voice. The identification data may further includeany information pertaining to the given purchaser of the one or morepurchasers other than the user, including, but not limited to,password(s), any contact information (e.g., address, phone numbers,e-mail addresses, etc.), demographic information (e.g., age, gender,marital status, income level, educational background, number of childrenin household, etc.), employment, and other data related to the givenpurchaser of the one or more purchasers other than the user.

Step 206 may include generating, via the one or more processors,anonymized transaction data based on the standardized transaction databy withholding (e.g., deleting, redacting, hiding, or separating) theidentification data. Withholding the identification data may includewithholding the identification data based on a sensitivity level of theidentification data. Such a sensitivity level of the identification datamay be set by, e.g., a user, a given purchaser, or one or morealgorithms. For instance, one or more algorithms may define that a nameof a given purchaser of the one or more purchasers other than the useras information with a high sensitivity level, and a merchant addressassociated with a certain purchase performed by the given purchaser ofthe one or more purchasers other than the user as information with a lowsensitivity level. In this situation, the name of the given purchasermay be withheld and the address may not be withheld. In another example,a medical history-related transaction (e.g., a user needs to buy certainprescribed medication at a specific location) of the given purchaser maybe defined as information with a high sensitivity level, and a timeassociated with a certain purchase performed by the given purchaser asinformation with a low sensitivity level. In this situation, enoughinformation to obscure the medical history of the given purchaser may bewithheld (e.g., the location and/or the specific prescribed medication),and other information (e.g., the time of purchase) may not be withheld.

Step 207 may include generating, via the one or more processors,presentable transaction data based on the anonymized transaction data.The presentable transaction data may include one or more prices or oneor more transaction locations associated with purchasing the product. Inthis situation, the presentable transaction data may be a list includingat least a price and respective location associated with the price ofpurchasing the product. A presentable transaction data layout may beshown in FIG. 4, as described elsewhere herein. The presentabletransaction data may include additional details associated withpurchasing the product, such as one or more rewards associated withpurchasing the product. Such rewards may include a cash back reward, adiscounted price, a customer loyalty reward, an incentive to promote theproduct, a new customer incentive, or an incentive associated with aparticular level or degree of engagement between the purchaser and theproduct or brand obtained at the time of purchasing the product. Thepresentable transaction data may additionally or alternatively include atax associated with purchasing the product. In some embodiments, a usermay be able to select how information in the layout is displayed. Forinstance, a user may select to display tax information separately fromthe actual price of the product (e.g., “price+tax”). In another example,the user may select to display the price of the product as thecombination of the actual price and tax. The tax levied for the productmay be different in different transaction locations in which the productis sold.

The presentable transaction data may further include any informationregarding the one or more purchasers other than the user, including, butnot limited to, transaction preferences of a purchaser (preferences orreviews regarding favorite products and/or services, favorite departmentstores, etc.), and previous transaction information associated with thepurchaser. The previous transaction information regarding the one ormore purchasers other than the user may include a prior transactiontime, a prior transaction location, spending profile of the one or morepurchasers other than the user, past spending levels on goods/servicessold by various manufacturers or merchants, a frequency of shopping bythe one or more purchasers other than the user at one or more retailoutlets, how much the one or more purchasers other than the user spendin an average transaction, how much the one or more purchasers otherthan the user have spent on a particular collection/category, how oftenthe one or more purchasers other than the user shop in a particularstore or kind of store, or online or offline stores at which the one ormore purchasers other than the user have purchased items.

Prior to generating the presentable transaction data, or at any stage ofproviding presentable transaction data, the method may further includeobtaining one or more selection criteria from the user. The one or moreselection criteria may include at least one of a product name, a timerange, or a geographic area associated with purchasing the product. Theone or more selection criteria may further include any criteria the usermay use to filter or select presentable transaction data, including, butnot limited to, a product category (e.g., beauty), a preferred language(e.g., English), a preferred delivery method (e.g., delivery orpick-up), a preferred purchasing method (e.g., cash or credit card), arecommendation level (e.g., a review of the product), a brand name, anappearance (e.g., color of the product), a price range, or a demographicrange of purchasers other than the user (e.g., age range). Such one ormore selection criteria from the user may be obtained via a displayscreen of a device associated with the user. The one or more selectioncriteria may be displayed on the display screen in any suitable form,such as an e-mail, a text message, a push presentable transaction data,content on a web page, and/or any form of graphical user interface. Thedevice associated with the user may be capable of accepting inputs ofthe user via one or more interactive components of the user/purchaserdevice 150, such as a keyboard, button, mouse, touchscreen, touchpad,joystick, trackball, camera, microphone, or motion sensor. For instance,a user may type a product name in a search box presented on a userinterface via a keyboard, or select a time rage among a plurality timeranges presented on a user interface via a mouse. A selection criterialayout may be shown in FIG. 4, as described elsewhere herein.

The method may include generating, via the one or more processors, thepresentable transaction data based on the anonymized transaction dataand the one or more selection criteria. One or more algorithms may beused to generate the presentable transaction data based on theanonymized transaction data and the one or more selection criteria. Theone or more algorithms may filter the anonymized transaction data basedon the one or more selection criteria. For instance, if the one or moreselection criteria include a product name that is an article ofclothing, a time range that is within last 3 months, or a geographicarea that is within 30 miles of a user's home address, then thepresentable transaction data may include information associated withpurchasing the article of clothing that is within last 3 months andwithin 30 miles of a user's home address.

Step 208 may include transmitting, to a device associated with the user,the presentable transaction data. The presentable transaction data maybe configured to be displayed on a display screen of a device associatedwith the user. The presentable transaction data may be displayed on thedisplay screen in any suitable form, such as an e-mail, a text message,a push presentable transaction data, content on a web page, and/or anyform of graphical user interface. The device associated with the usermay be capable of accepting inputs of the user via one or moreinteractive components of the user/purchaser device 150, such as akeyboard, button, mouse, touchscreen, touchpad, joystick, trackball,camera, microphone, or motion sensor. The inputs of the user may includeadditional selection criteria.

The method may further include, e.g., generating, via the one or moreprocessors, a purchasing recommendation based on the anonymizedtransaction data via a trained machine learning algorithm. Thepurchasing recommendation may inform a user of a potential favorableprice (e.g., a lower price than average, or a discount on a currentprice) for a product during a given time, such as a present time windowor a time in the future. The purchasing recommendation may include,e.g., at least one of a time or time window, a price, and a locationassociated with purchasing the product. For instance, based on theanonymized transaction data associated with one or more purchasers otherthan the user, a purchasing recommendation informing a user to purchasea product at a future time and future location may be provided to theuser. Details of the machine learning algorithm are described elsewhereherein. In some embodiments, not all of the steps 201-208 of the methodmay be performed. For instance, instead of obtaining preliminarytransaction data from the one or more purchasers, the preliminarytransaction data may be already stored in one or more databases, so astep of obtaining preliminary transaction data may include obtainingpreliminary transaction from one or more databases (e.g., databasesassociated with a financial services provider).

FIG. 3 is a flowchart illustrating another exemplary method forproviding presentable transaction data of a product to a user, accordingto one or more embodiments of the present disclosure. The method may beperformed by, e.g., computer system 110 (e.g., the algorithm model 112),the network 130, the one or more data resources 140, and/or theuser/purchaser device 150.

Step 301, similarly to step 201 of FIG. 2, may include obtaining, viaone or more processors, preliminary transaction data of one or morepurchasers other than the user. The preliminary transaction data of theone or more purchasers other than the user may include one or morepurchasing records, such as partial or full receipts, statements, orother records associated with purchasing the product. The preliminarytransaction data may include reward data associated with purchasing theproduct. The step of obtaining the preliminary transaction data mayinclude providing one or more incentives to the one or more purchasersother than the user. The step of obtaining the preliminary transactiondata may include enabling the one or more purchaser other than the userto register with a platform (e.g., an app on a device associated with apurchaser) provided by one or more data resources 140, obtainingidentification data of the one or more purchaser other than the user,determining that the one or more purchasers have registered with theplatform, enabling the one or more purchasers to perform transactionsassociated with purchasing the product, and/or gathering the preliminarytransaction data associated with transactions associated with purchasingthe product via the platform. For instance, a financial servicesprovider may provide an app on a device to enable the purchaser topurchase the product via this app (e.g., online or presenting a digitalcredit card to purchasing the product), and the purchaser may registerwith the app and use the app to purchase the product. In this situation,the app may facilitate gathering the preliminary data associated withpurchasing the product performed by the purchaser and forward suchpreliminary data to one or more databases of the financial servicesprovider. Details of the preliminary transaction data and obtaining thepreliminary transaction data are described elsewhere herein.

Step 302, similarly to step 202 of FIG. 2, may include generating, viathe one or more processors, itemized transaction data based on thepreliminary transaction data. The itemized transaction data may includeone or more data categories associated with purchasing the product. Theone or more data categories may include at least one of a location, aprice, or a time associated with purchasing the product. In someembodiments, the itemized transaction data may not include a location ora time associated with purchasing the product. In this situation, suchinformation (a location or a time) may be added via one or morealgorithms provided by the one or more resources that are involved inpurchasing the product. Details of the itemized transaction data and theone or more data categories are described elsewhere herein.

Step 303, similarly to step 203 of FIG. 2, may include obtaining, viathe one or more processors, one or more translation codes from one ormore transaction entities. The one or more transaction entities mayinclude one or more enhanced merchant service agencies. Step 304,similarly to step 204 of FIG. 2, may include generating, via the one ormore processors, standardized transaction data based on the itemizedtransaction data and the one or more translation codes. Details of thetranslation codes, one or more transaction entities, and standardizedtransaction data are described elsewhere herein.

Step 305, similarly to step 205 of FIG. 2, may include retrieving, viathe one or more processors, identification data from the standardizedtransaction data. The identification data may include, for example, oneor more of a name or an account number of a given purchaser of the oneor more purchasers other than the user. The identification data mayfurther include a time associated with purchasing the product. Step 306,similarly to step 206 of FIG. 2, may include generating, via the one ormore processors, anonymized transaction data based on the standardizedtransaction data by withholding the identification data. Withholding theidentification data may include withholding the identification databased on a sensitivity level of the identification data. Details of theidentification data, the sensitivity level and the anonymizedtransaction data are described elsewhere herein.

Step 307 may include obtaining, via the one or more processors, one ormore selection criteria from the user. The one or more selectioncriteria may include at least one of a product name, a time range, or ageographic area associated with purchasing the product. Details of theone or more selection criteria are described elsewhere herein. Step 308may include generating, via the one or more processors, presentabletransaction data based on the anonymized transaction data and the one ormore selection criteria. The presentable transaction data may includeone or more prices or one or more transaction locations associated withpurchasing the product. The presentable transaction data may include oneor more rewards associated with purchasing the product. The presentabletransaction data may include a tax associated with purchasing theproduct. Details of the presentable transaction data are describedelsewhere herein.

Step 309 may include generating, via the one or more processors, apurchasing recommendation based on the anonymized transaction data via atrained machine learning algorithm. The purchasing recommendation mayinclude at least one of a future time, a future price, and/or a futurelocation associated with purchasing the product. For instance, based onthe anonymized transaction data associated with one or more purchasersother than the user, a purchasing recommendation informing a user topurchase a product at a future time and future location may be providedto the user. In this situation, the user may set up a timed notification(e.g., a notification that will be sent to the user right before thefuture time). Details of the machine learning algorithm are describedelsewhere herein.

Step 310, similarly to step 208, may include transmitting, to a deviceassociated with the user, the presentable transaction data and thepurchasing recommendation. The presentable transaction data and thepurchasing recommendation may be configured to be displayed on a displayscreen of a device associated with the user. The presentable transactiondata and the purchasing recommendation may be displayed on the displayscreen in any suitable form, such as an e-mail, a text message, a pushpresentable transaction data, content on a web page, and/or any form ofgraphical user interface. The device associated with the user may becapable of accepting inputs of the user via one or more interactivecomponents of the user/purchaser device 150, such as a keyboard, button,mouse, touchscreen, touchpad, joystick, trackball, camera, microphone,or motion sensor. The inputs of the user may include one or moreselection criteria. Details of the one or more selection criteria aredescribed elsewhere herein.

FIG. 4 shows a graphical representation of an exemplary user interface400 that may be provided on the user/purchaser device 150 of FIG. 1,and/or as a part of the methods of FIGS. 2 and/or 3. The user interface400 may be displayed as, e.g., a website on an internet browser, adisplay on a mobile app, and/or any other type of display suitable foruser interaction. The user interface 400 may be displayed to the user sothe user can choose one or more selection criteria. The format andvisual characteristics of the user interface 400 are exemplary, and inother embodiments, similar information illustrated in FIG. 4 may bepresented in a different format via software executing on an electronicdevice (e.g., a desktop, mobile phone, or tablet computer) serving asthe user/purchaser device 150.

The user interface 400 may include one or more layouts, any or each ofwhich may have interactive components. The one or more layouts mayinclude, e.g., a selection criteria layout 402, and/or a presentabletransaction data layout 404. The selection criteria layout 402 mayenable the user to select criteria relating to a transaction (e.g., apotential transaction), including, but not limited to, a product name, atime range associated with purchasing the product, and/or a geographicarea associated with purchasing the product. In some embodiments,selection criteria may be automatically filled in for a user. In theexample depicted in FIG. 4, for example, the selection criteria layout402 may present an item name (e.g., DVD), a time range (e.g., past 24hours), group criteria (e.g., the presentable transaction data isgrouped by merchant), a method of purchase (e.g., online purchase),and/or a geographic area (e.g., within 15 miles). Any additional oralternative selection criteria may also be presented, such as a targetprice, a shipping rate, hours of merchant operation, a merchant type(e.g., a franchise or a small business), etc. The presentabletransaction data layout 404 may show any information regardingpresentable transaction data associated with purchasing a product basedon the one or more selection criteria and/or the preliminary transactiondata. In this example, the presentable transaction data may include dataobtained within a time range of a past 24 hours, within 15 miles of alocation (e.g., a location of the user device or of the user, or anotherlocation), grouped by merchant, and including online purchaseinformation. The presentable transaction data may include prices andrespective transaction locations associated with purchasing a productsharing an item name with the item name specified in the selectioncriteria. Additionally, the user interface may include one or moregraphical elements, including, but not limited to, input controls (e.g.,checkboxes, radio buttons, dropdown lists, list boxes, buttons, toggles,text fields, date field), navigational components (e.g., breadcrumb,slider, search field, pagination, slider, tags, icons), informationalcomponents (e.g., tooltips, icons, progress bar, notifications, messageboxes, modal windows), or containers (e.g., accordion). Any suitablecombination of graphical elements may be used in the presentation of theuser interface 400.

At any stage of providing presentable transaction data of a product to auser, methods disclosed herein may further include retrieving priorpreliminary transaction data associated with purchasing the product,prior itemized transaction data associated with purchasing the product,prior one or more translation codes associated with purchasing theproduct, prior standardized transaction data associated with purchasingthe product, prior identification data associated with one or morepurchasers other than the user who purchase the product, prioranonymized transaction data associated with purchasing the product,prior one or more selection criteria provided by the user associatedwith purchasing the product, prior presentable transaction dataassociated with purchasing the product, and/or a prior purchasingrecommendation associated with purchasing the product; and determiningcurrent or future presentable transaction data or current or futurepurchasing recommendation to the user via a trained machine learningalgorithm. The prior preliminary transaction data associated withpurchasing the product, prior itemized transaction data associated withpurchasing the product, prior one or more translation codes associatedwith purchasing the product, prior standardized transaction dataassociated with purchasing the product, prior identification dataassociated with one or more purchasers other than the user who purchasethe product, prior anonymized transaction data associated withpurchasing the product, prior one or more selection criteria provided bythe user associated with purchasing the product, prior presentabletransaction data associated with purchasing the product, and/or theprior purchasing recommendation associated with purchasing the productmay be stored in a non-transitory computer-readable medium or one ormore databases. The current or future presentable transaction data orcurrent or future purchasing recommendation may be configured to bedisplayed on a display screen of the device associated with the user(e.g., user/purchaser device 150).

The trained machine learning algorithm may include, e.g., aregression-based model that accepts prior preliminary transaction dataassociated with purchasing the product, prior itemized transaction dataassociated with purchasing the product, prior one or more translationcodes associated with purchasing the product, prior standardizedtransaction data associated with purchasing the product, prioridentification data associated with one or more purchasers other thanthe user who purchased the product, prior anonymized transaction dataassociated with purchasing the product, prior one or more selectioncriteria provided by the user associated with purchasing the product,prior presentable transaction data associated with purchasing theproduct, and/or the prior purchasing recommendation associated withpurchasing the product as input data. In some embodiments, the trainedmachine learning algorithm may be part of the algorithm model 112. Thetrained machine learning algorithm may be of any suitable form, and mayinclude, for example, a neural network. A neural network may be softwarerepresenting human neural system (e.g., cognitive system). A neuralnetwork may include a series of layers termed “neurons” or “nodes.” Aneural network may comprise an input layer, to which data is presented;one or more internal layers; and an output layer. The number of neuronsin each layer may be related to the complexity of a problem to besolved. Input neurons may receive data being presented and then transmitthe data to the first internal layer through connections' weight. Aneural network may include a convolutional neural network, a deep neuralnetwork, or a recurrent neural network.

The trained machine learning algorithm may compute current or futurepresentable transaction data or current or future purchasingrecommendation to the user as a function of prior preliminarytransaction data associated with purchasing the product, prior itemizedtransaction data associated with purchasing the product, prior one ormore translation codes associated with purchasing the product, priorstandardized transaction data associated with purchasing the product,prior identification data associated with one or more purchasers otherthan the user who purchase the product, prior anonymized transactiondata associated with purchasing the product, prior one or more selectioncriteria provided by the user associated with purchasing the product,prior presentable transaction data associated with purchasing theproduct, and/or the prior purchasing recommendation associated withpurchasing the product, or one or more variables indicated in the inputdata. The one or more variables may be derived from the priorpreliminary transaction data associated with purchasing the product,prior itemized transaction data associated with purchasing the product,prior one or more translation codes associated with purchasing theproduct, prior standardized transaction data associated with purchasingthe product, prior identification data associated with one or morepurchasers other than the user who purchase the product, prioranonymized transaction data associated with purchasing the product,prior one or more selection criteria provided by the user associatedwith purchasing the product, prior presentable transaction dataassociated with purchasing the product, and/or the prior purchasingrecommendation associated with purchasing the product. This function maybe learned by training the machine learning algorithm with trainingsets.

The machine learning algorithm may be trained by supervised,unsupervised or semi-supervised learning using training sets comprisingdata of types similar to the type of data used as the model input. Forexample, the training set used to train the model may include anycombination of the following: prior preliminary transaction dataassociated with purchasing the product, prior itemized transaction dataassociated with purchasing the product, prior one or more translationcodes associated with purchasing the product, prior standardizedtransaction data associated with purchasing the product, prioridentification data associated with one or more purchasers other thanthe user who purchase the product, prior anonymized transaction dataassociated with purchasing the product, prior one or more selectioncriteria provided by the user associated with purchasing the product,prior presentable transaction data associated with purchasing theproduct, and/or the prior purchasing recommendation associated withpurchasing the product. Additionally, the training set used to train themodel may further include user/purchaser data, including, but notlimited to, an actual name, contact information (e.g., address, phonenumbers, e-mail addresses, etc.), and other data related to the user orpurchaser.

In general, any process discussed in this disclosure that is understoodto be computer-implementable, such as the processes illustrated in FIGS.2-3, may be performed by one or more processors of a computer system,such as computer system 110, as described above. A process or processstep performed by one or more processors may also be referred to as anoperation. The one or more processors may be configured to perform suchprocesses by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or any suitable types of processing unit.

A computer system, such as computer system 110 and/or user/purchaserdevice 150, may include one or more computing devices. If the one ormore processors of the computer system 110 and/or user/purchaser device150 are implemented as a plurality of processors, the plurality ofprocessors may be included in a single computing device or distributedamong a plurality of computing devices. If a computer system 110 and/oruser/purchaser device 150 comprises a plurality of computing devices,the memory of the computer system 110 may include the respective memoryof each computing device of the plurality of computing devices.

FIG. 5 illustrates an example of a computing device 500 of a computersystem, such as computer system 110 and/or user/purchaser device 150.The computing device 500 may include processor(s) 510 (e.g., CPU, GPU,or other such processing unit(s)), a memory 520, and communicationinterface(s) 540 (e.g., a network interface) to communicate with otherdevices. Memory 520 may include volatile memory, such as RAM, and/ornon-volatile memory, such as ROM and storage media. Examples of storagemedia include solid-state storage media (e.g., solid state drives and/orremovable flash memory), optical storage media (e.g., optical discs),and/or magnetic storage media (e.g., hard disk drives). Theaforementioned instructions (e.g., software or computer-readable code)may be stored in any volatile and/or non-volatile memory component ofmemory 520. The computing device 500 may, in some embodiments, furtherinclude input device(s) 550 (e.g., a keyboard, mouse, or touchscreen)and output device(s) 560 (e.g., a display, printer). The aforementionedelements of the computing device 500 may be connected to one anotherthrough a bus 530, which represents one or more busses. In someembodiments, the processor(s) 510 of the computing device 500 includesboth a CPU and a GPU.

Instructions executable by one or more processors may be stored on anon-transitory computer-readable medium. Therefore, whenever acomputer-implemented method is described in this disclosure, thisdisclosure shall also be understood as describing a non-transitorycomputer-readable medium storing instructions that, when executed by oneor more processors, cause the one or more processors to perform thecomputer-implemented method. Examples of non-transitorycomputer-readable medium include RAM, ROM, solid-state storage media(e.g., solid state drives), optical storage media (e.g., optical discs),and magnetic storage media (e.g., hard disk drives). A non-transitorycomputer-readable medium may be part of the memory of a computer systemor separate from any computer system.

It should be appreciated that in the above description of exemplaryembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various inventive aspects. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaims require more features than are expressly recited in each claim.Rather, as the following claims reflect, inventive aspects lie in lessthan all features of a single foregoing disclosed embodiment. Thus, theclaims following the Detailed Description are hereby expresslyincorporated into this Detailed Description, with each claim standing onits own as a separate embodiment of this disclosure.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe disclosure, and form different embodiments, as would be understoodby those skilled in the art. For example, in the following claims, anyof the claimed embodiments can be used in any combination.

Thus, while certain embodiments have been described, those skilled inthe art will recognize that other and further modifications may be madethereto without departing from the spirit of the disclosure, and it isintended to claim all such changes and modifications as falling withinthe scope of the disclosure. For example, functionality may be added ordeleted from the block diagrams and operations may be interchanged amongfunctional blocks. Steps may be repeated, added to, or deleted frommethods described within the scope of the present disclosure.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted.

1-20. (canceled)
 21. A computer-implemented method for providing pricecomparison data to a user device associated with a user, the methodcomprising: obtaining, via at least one processor, preliminarytransaction data including one or more receipts corresponding to one ormore executed purchases of a product by one or more persons other thanthe user; for each of the one or more receipts, identifying, via the atleast one processor: (i) one or more of a location, a price, or a timeassociated with a corresponding executed purchase, and (ii) personallyidentifying information associated with the person that executed thecorresponding purchase; generating, via the at least one processor,anonymized transaction data based on the preliminary transaction data bywithholding and/or excluding the identified personally identifyinginformation, wherein the anonymized transaction information includes theidentified one or more location, price, or time associated with the oneor more executed purchases by the one or more persons other than theuser; and transmitting the anonymized transaction data to the userdevice.
 22. The computer-implemented method of claim 21, furthercomprising: for each of the one or more receipts, identifying, via theat least one processor, a merchant associated with a correspondingexecuted purchase.
 23. The computer-implemented method of claim 21,wherein the preliminary transaction data includes reward data associatedwith the one or more executed purchases.
 24. The computer-implementedmethod of claim 21, wherein the anonymized transaction data includesreward data associated with purchasing the product.
 25. Thecomputer-implemented method of claim 21, wherein obtaining thepreliminary transaction data includes providing one or more incentivesto the one or more persons other than the user.
 26. Thecomputer-implemented method of claim 21, wherein the anonymizedtransaction data includes a tax associated with purchasing the product.27. The computer-implemented method of claim 21, further comprising:receiving one or more selection criteria from the user device, whereinthe one or more selection criteria includes at least one of a productname, a time range, or a geographic area associated with purchasing theproduct.
 28. The computer-implemented method of claim 27, furthercomprising: prior to transmitting the anonymized transaction data,filtering the anonymized transaction data based on the one or moreselection criteria.
 29. The computer-implemented method of claim 21,further comprising: generating, via the at least one processor, apurchasing recommendation based on the anonymized transaction data via atrained machine learning algorithm, wherein the purchasingrecommendation includes at least one of a future time, a future price,and a future location associated with purchasing the product.
 30. Thecomputer-implemented method of claim 21, further comprising: determininga sensitivity level of the personally identifying information associatedwith the person that executed the corresponding purchase; wherein, ineach case, the withholding and/or excluding the identified personallyidentifying information is performed based on the determined sensitivitylevel.
 31. A system for providing price comparison data to a user deviceassociated with a user, comprising: a processor; and a memory storinginstructions that are executable by the processor to perform acts,including: obtaining preliminary transaction data including one or morereceipts corresponding to one or more executed purchases of a product byone or more persons other than the user; for each of the one or morereceipts, identifying: (i) one or more of a location, a price, or a timeassociated with a corresponding executed purchase, and (ii) personallyidentifying information associated with the person that executed thecorresponding purchase; generating, anonymized transaction data based onthe preliminary transaction data by withholding and/or excluding theidentified personally identifying information, wherein the anonymizedtransaction information includes the identified one or more location,price, or time associated with the one or more executed purchases by theone or more persons other than the user; and transmitting the anonymizedtransaction data to the user device.
 32. The system of claim 31, whereinthe acts further include: for each of the one or more receipts,identifying a merchant associated with a corresponding executedpurchase.
 33. The system of claim 31, wherein the preliminarytransaction data includes reward data associated with the one or moreexecuted purchases.
 34. The system of claim 31, wherein the anonymizedtransaction data includes reward data associated with purchasing theproduct.
 35. The system of claim 31, wherein obtaining the preliminarytransaction data includes providing one or more incentives to the one ormore persons other than the user.
 36. The system of claim 31, whereinthe anonymized transaction data includes a tax associated withpurchasing the product.
 37. The system of claim 31, wherein the actsfurther include: receiving one or more selection criteria from the userdevice, wherein the one or more selection criteria includes at least oneof a product name, a time range, or a geographic area associated withpurchasing the product; and prior to transmitting the anonymizedtransaction data, filtering the anonymized transaction data based on theone or more selection criteria.
 38. The system of claim 31, wherein theacts further include: generating, via the processor, a purchasingrecommendation based on the anonymized transaction data via a trainedmachine learning algorithm, wherein the purchasing recommendationincludes at least one of a future time, a future price, and a futurelocation associated with purchasing the product.
 39. The system of claim31, wherein the acts further include: determining a sensitivity level ofthe personally identifying information associated with the person thatexecuted the corresponding purchase; wherein, in each case, thewithholding and/or excluding the identified personally identifyinginformation is performed based on the determined sensitivity level. 40.A computer-implemented method for providing price comparison data to auser device associated with a user, the method comprising: obtaining,via at least one processor, preliminary transaction data including oneor more receipts corresponding to one or more executed purchases of aproduct by one or more persons other than the user; for each of the oneor more receipts, identifying, via the at least one processor: (i) oneor more of a merchant, a location, a price, or a time associated with acorresponding executed purchase, and (ii) personally identifyinginformation associated with the person that executed the correspondingpurchase; generating, via the at least one processor, anonymizedtransaction data based on the preliminary transaction data bywithholding and/or excluding the identified personally identifyinginformation, wherein the anonymized transaction information includes theidentified one or more merchant, location, price, or time associatedwith the one or more executed purchases by the one or more persons otherthan the user; generating, via the at least one processor, a purchasingrecommendation based on the anonymized transaction data, wherein thepurchasing recommendation includes at least one of a future time, afuture price, and a future location associated with purchasing theproduct; and transmitting the anonymized transaction data and thepurchasing recommendation to the user device.